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Improving Likelihood-Ratio-Based Confidence Intervals for Threshold Parameters in Finite Samples

Author

Listed:
  • Donayre, Luiggi
  • Eo, Yunjong
  • Morley, James
Abstract
Within the context of threshold regressions, we show that asymptotically-valid likelihood-ratio-based confidence intervals for threshold parameters perform poorly in finite samples when the threshold effect is large. A large threshold effect leads to a poor approximation of the profile likelihood in finite samples such that the conventional approach to constructing confidence intervals excludes the true threshold parameter value too often, resulting in low coverage rates. We propose a conservative modification to the standard likelihood-ratio-based confidence interval that has coverage rates at least as high as the nominal level, while still being informative in the sense of including relatively few observations of the threshold variable. An application to thresholds for U.S. industrial production growth at a disaggregated level shows the empirical relevance of applying the proposed approach..

Suggested Citation

  • Donayre, Luiggi & Eo, Yunjong & Morley, James, 2014. "Improving Likelihood-Ratio-Based Confidence Intervals for Threshold Parameters in Finite Samples," Working Papers 2014-04, University of Sydney, School of Economics.
    • Handle: RePEc:syd:wpaper:2014-04
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    File URL: http://econ-wpseries.com/2014/201404-05.pdf
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    References listed on IDEAS

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    Cited by:

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    3. Nebot, César & Beyaert, Arielle & García-Solanes, José, 2019. "New insights into the nonlinearity of Okun's law," Economic Modelling, Elsevier, vol. 82(C), pages 202-210.

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    More about this item

    Keywords

    Threshold regression; Inverted likelihood ratio; Confidence Interval; Finite-sample inference;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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